Paul-Andrei Dragan, in collaboration with Prof. Dr. Andreas Metzger and Prof. Dr. Klaus Pohl, introduces the CoADAPT approach, a decentralized method for coordinating multiple self-adaptive systems. CoADAPT enables the coordination of adaptations within a shared environment without the necessity of sharing all adaptation specifics. Consequently, individual adaptation preferences of self-adaptive systems can be kept private, while information about possible adaptation conflicts can be exchanged. This functionality proves to be particularly valuable for, among other things, the protection of privacy in the realm of cloud computing applications.

Paul-Andrei Dragan presented the paper on 27 September 2023 at the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) in Toronto.

Abstract:

Paul-Andrei Dragan, Andreas Metzger, and Klaus Pohl: Towards the decentralized coordination of multiple
self-adaptive systems

When multiple self-adaptive systems share an environment and goals, they may coordinate their adaptations to avoid conflicts and satisfy their goals. There are two approaches to coordination. (1) Logically centralized, where a supervisor has complete control over the self-adaptive systems. Such an approach is infeasible when the systems have different owners or administrative domains. (2) Logically decentralized, where coordination is achieved  through direct interactions. Because the individual systems have control over the information they share, decentralized coordination accommodates multiple administrative domains. However, existing techniques do not account simultaneously for local concerns, e.g., preferences, and shared concerns, e.g., conflicts, which may lead to goals not being achieved as expected. We address this shortcoming by expressing both types of concerns within one constraint optimization problem. Our technique, CoADAPT, introduces two types of constraints: preference constraints, expressing local concerns, and consistency constraints, expressing shared concerns. At runtime, the problem is solved in a decentralized way using distributed constraint optimization algorithms. As a first step in realizing CoADAPT, we focus on the coordination of adaptation planning strategies, traditionally addressed only with centralized techniques. We show the feasibility of CoADAPT in an exemplar from cloud computing and analyze experimentally its scalability.